用python或Pyspark数据框中的特殊字符重命名列 [英] Rename columns with special characters in python or Pyspark dataframe

问题描述

我在python/pyspark中有一个数据框.这些列具有特殊字符，例如点(.)，空格，括号(())和括号{}.用他们的名字.

I have a data frame in python/pyspark. The columns have special characters like dot(.) spaces brackets(()) and parenthesis {}. in their names.

现在，我想以这样的方式重命名列名称:如果有点和空格，请用下划线替换它们，如果有()和{}，则将其从列名称中删除.

Now I want to rename the column names in such a way that if there are dot and spaces replace them with underscore and if there are () and {} then remove them from the column names.

我已经完成了

df1 = df.toDF(*(re.sub(r'[\.\s]+', '_', c) for c in df.columns))

通过此操作，我能够用不能执行第二位的下划线替换点和空格，即如果()和{}在那里，则将它们从列名称中删除.

with this I was able to replace the dot and spaces with underscores with Unable to do the second bit i.e if () and {} are there just remove them form column names.

我们如何实现这一目标.

How do we achieve that.

推荐答案

如果拥有pyspark数据框，则可以尝试使用withColumnRenamed函数重命名列.我确实尝试过，看看并为您的更改自定义它.

If you are having a pyspark dataframe, you can try using withColumnRenamed function to rename the columns. I did try in my way, have a look and customize it for your changes.

>>> l=[('some value1','some value2','some value 3'),('some value4','some value5','some value 6')]
>>> l_schema = StructType([StructField("col1.some valwith(in)and{around}",StringType(),True),StructField("col2.some valwith()and{}",StringType(),True),StructField("col3 some()valwith.and{}",StringType(),True)])
>>> reps=('.','_'),(' ','_'),('(',''),(')',''),('{','')('}','')
>>> rdd = sc.parallelize(l)
>>> df = sqlContext.createDataFrame(rdd,l_schema)
>>> df.printSchema()
root
 |-- col1.some valwith(in)and{around}: string (nullable = true)
 |-- col2.some valwith()and{}: string (nullable = true)
 |-- col3 some()valwith.and{}: string (nullable = true)

>>> df.show()
+------------------------+------------------------+------------------------+
|col1.some valwith(in)and{around}|col2.some valwith()and{}|col3 some()valwith.and{}|
+------------------------+------------------------+------------------------+
|             some value1|             some value2|            some value 3|
|             some value4|             some value5|            some value 6|
+------------------------+------------------------+------------------------+

>>> def colrename(x):
...    return reduce(lambda a,kv : a.replace(*kv),reps,x)
>>> for i in df.schema.names:
...    df = df.withColumnRenamed(i,colrename(i))
>>> df.printSchema()
root
 |-- col1_some_valwithinandaround: string (nullable = true)
 |-- col2_some_valwithand: string (nullable = true)
 |-- col3_somevalwith_and: string (nullable = true)

>>> df.show()
+--------------------+--------------------+--------------------+
|col1_some_valwithinandaround|col2_some_valwithand|col3_somevalwith_and|
+--------------------+--------------------+--------------------+
|                 some value1|         some value2|        some value 3|
|                 some value4|         some value5|        some value 6|
+--------------------+--------------------+--------------------+

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